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JMLR
2010
137views more  JMLR 2010»
14 years 4 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
CVPR
2005
IEEE
15 years 11 months ago
A Hybrid Graphical Model for Robust Feature Extraction from Video
We consider a visual scene analysis scenario where objects (e.g. people, cars) pass through the viewing field of a static camera and need to be detected and segmented from the bac...
Ali Taylan Cemgil, Wojciech Zajdel, Ben J. A. Kr&o...
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JMLR
2010
125views more  JMLR 2010»
14 years 4 months ago
Variational methods for Reinforcement Learning
We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transit...
Thomas Furmston, David Barber
ECAI
2010
Springer
14 years 10 months ago
EP for Efficient Stochastic Control with Obstacles
Abstract. We address the problem of continuous stochastic optimal control in the presence of hard obstacles. Due to the non-smooth character of the obstacles, the traditional appro...
Thomas Mensink, Jakob J. Verbeek, Bert Kappen
CORR
2006
Springer
123views Education» more  CORR 2006»
14 years 9 months ago
Counting good truth assignments of random k-SAT formulae
We present a deterministic approximation algorithm to compute logarithm of the number of `good' truth assignments for a random k-satisfiability (k-SAT) formula in polynomial ...
Andrea Montanari, Devavrat Shah